Source code for flyqma.annotation.classification.kmeans

import numpy as np
from sklearn.cluster import KMeans

from .classifiers import Classifier

[docs]class KMeansClassifier(Classifier): """ K-means classifier. Attributes: groups (dict) - {cluster_id: label_id} pairs for merging clusters component_to_label (vectorized func) - maps cluster_id to label_id km (sklearn.cluster.KMeans) - kmeans object classifier (vectorized func) - maps value to label_id labels (np.ndarray[int]) - predicted labels Inherited attributes: values (array like) - basis for clustering attribute (str or list) - attribute(s) on which to cluster log (bool) - indicates whether clustering performed on log values cmap (matplotlib.colors.ColorMap) - colormap for label_id parameters (dict) - {param name: param value} pairs fig (matplotlib.figures.Figure) - histogram figure """ def __init__(self, values, num_components=3, groups=None, log=True, **kwargs): """ Instantiate k-means classifier. Args: values (array like) - basis for clustering num_components (int) - number of clusters groups (dict) - {cluster_id: label_id} pairs for merging clusters log (bool) - indicates whether clustering performed on log values kwargs: keyword arguments for Classifier parent class """ # set groups and number of clusters if groups is None: groups = {k: k for k in range(num_components)} else: groups = {int(k): v for k, v in groups.items()} num_labels = len(groups) # instantiate classifier super().__init__(values, num_labels=num_labels, log=log, **kwargs) self.num_components = num_components self.component_to_label = np.vectorize(groups.get) self.groups = groups # build classifiers self.model =, self.num_components) self.classifier = self._build_value_to_groups_classifier() # assign group labels self.labels = self.classifier(self.values.reshape(-1, 1)) # store parameters self.parameters.update(dict(groups=self.groups)) @property def means(self): """ Mean of each cluster. """ return self.model.cluster_centers_.ravel()
[docs] def predict(self, values): """ Predict which component each of <values> belongs to. """ return self.model.predict(values)
[docs] @staticmethod def fit(values, n): """ Fit n clusters to x """ return KMeans(n).fit(values.reshape(-1, 1))
@staticmethod def _build_value_to_cluster_classifier(km): """ Build classifier mapping values to sequential clusters. """ centroids = km.cluster_centers_.ravel() flip = lambda f: f.__class__(map(reversed, f.items())) km_to_ordered_dict = flip(dict(enumerate(np.argsort(centroids)))) km_to_ordered = np.vectorize(km_to_ordered_dict.get) classifier = lambda x: km_to_ordered(km.predict(x)) return classifier def _build_value_to_groups_classifier(self): """ Build classifier mapping values to groups. """ value_to_cluster = self._build_value_to_cluster_classifier(self.model) classifier = lambda x: self.component_to_label(value_to_cluster(x)) return classifier